Automatic Fault Diagnosis Model for Helicopter Main Retarders Based on Fuzzy Neural Network

Helicopter is a complex aircraft, its main reducer is a key transmission component, and its health status directly affects the overall performance of the helicopter. Therefore, it is important to develop an efficient automatic fault diagnosis model to ensure the safe operation of the helicopter. In this research, a fuzzy neural network (FNN) is used as the core algorithm, combining the advantages of fuzzy logic and neural network, to realize the intelligent identification and diagnosis of helicopter main retarder fault. First, a fault database of the helicopter main reducer is constructed, including vibration signals and temperature data of various common fault types. Then, signal processing technology such as wavelet transform is used to pre-process the original data and extract effective fault features. These features are used as inputs of the FNN model, and through fuzzy processing, continuous data is converted into fuzzy sets. In order to adapt to the reasoning process of fuzzy logic, the network structure and parameters are gradually optimized through training and learning to realize the accurate identification and classification of fault characteristics. The experimental results show that the model has high accuracy and robustness in the fault diagnosis of helicopter main retarders, and can effectively identify and distinguish different types of faults. The research in this paper not only provides a new idea and method for the fault diagnosis of helicopter main retarders, but also further promotes the application of FNN in the field of mechanical fault diagnosis.